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soda/core/soda/sodacl/anomaly_detection_metric_check_cfg.py

@@ -3,7 +3,6 @@
 from typing import Any, ClassVar, Optional, Union
 
 from pydantic import BaseModel, ConfigDict, ValidationError, field_validator
-
 from soda.common.logs import Logs
 from soda.sodacl.change_over_time_cfg import ChangeOverTimeCfg
 from soda.sodacl.location import Location

soda/core/soda/sodacl/sodacl_parser.py

@@ -14,8 +14,6 @@
 from soda.common.logs import Logs
 from soda.common.parser import Parser
 from soda.common.yaml_helper import to_yaml_str
-from soda.execution.identity import ConsistentHashBuilder
-from soda.model.type_mapping import TypeMapping
 from soda.sodacl.anomaly_detection_metric_check_cfg import (
     AnomalyDetectionMetricCheckCfg,
     ModelConfigs,

soda/core/tests/data_source/test_anomaly_detection_check.py

@@ -4,7 +4,6 @@
 from helpers.common_test_tables import customers_test_table
 from helpers.data_source_fixture import DataSourceFixture
 from helpers.mock_soda_cloud import TimeGenerator
-
 from soda.cloud.historic_descriptor import (
     HistoricCheckResultsDescriptor,
     HistoricMeasurementsDescriptor,
@@ -19,7 +18,6 @@ def test_anomaly_detection_historic_descriptors(data_source_fixture: DataSourceF
     table_name = data_source_fixture.ensure_test_table(customers_test_table)
 
     import numpy as np
-
     from soda.execution.check.anomaly_detection_metric_check import (
         AnomalyDetectionMetricCheck,
     )

soda/scientific/setup.py

@@ -3,11 +3,6 @@
 
 from setuptools import find_namespace_packages, setup
 
-if sys.version_info < (3, 8):
-    print("Error: Soda Core requires at least Python 3.8")
-    print("Error: Please upgrade your Python version to 3.8 or later")
-    sys.exit(1)
-
 package_name = "soda-core-scientific"
 package_version = "3.1.3"
 description = "Soda Core Scientific Package"

soda/scientific/soda/scientific/anomaly_detection_v2/anomaly_detector.py

@@ -3,8 +3,12 @@
 
 import pandas as pd
 import yaml
-
 from soda.common.logs import Logs
+from soda.sodacl.anomaly_detection_metric_check_cfg import (
+    ModelConfigs,
+    TrainingDatasetParameters,
+)
+
 from soda.scientific.anomaly_detection_v2.feedback_processor import FeedbackProcessor
 from soda.scientific.anomaly_detection_v2.models.prophet_model import ProphetDetector
 from soda.scientific.anomaly_detection_v2.pydantic_models import (
@@ -14,10 +18,6 @@
     AnomalyResult,
     FreqDetectionResult,
 )
-from soda.sodacl.anomaly_detection_metric_check_cfg import (
-    ModelConfigs,
-    TrainingDatasetParameters,
-)
 
 
 class AnomalyDetector:

soda/scientific/soda/scientific/anomaly_detection_v2/feedback_processor.py

@@ -8,7 +8,6 @@
 from typing import Any, Dict, Tuple
 
 import pandas as pd
-
 from soda.common.logs import Logs
 
 FEEDBACK_REASONS = {

soda/scientific/soda/scientific/anomaly_detection_v2/frequency_detector.py

@@ -1,8 +1,8 @@
 from typing import Any
 
 import pandas as pd
-
 from soda.common.logs import Logs
+
 from soda.scientific.anomaly_detection_v2.globals import DETECTOR_MESSAGES
 from soda.scientific.anomaly_detection_v2.pydantic_models import FreqDetectionResult
 from soda.scientific.anomaly_detection_v2.utils import (

soda/scientific/soda/scientific/anomaly_detection_v2/models/base.py

@@ -2,8 +2,8 @@
 from abc import ABC, abstractmethod
 
 import pandas as pd
-
 from soda.common.logs import Logs
+
 from soda.scientific.anomaly_detection_v2.pydantic_models import FreqDetectionResult
 
 

soda/scientific/soda/scientific/anomaly_detection_v2/models/prophet_model.py

@@ -11,9 +11,14 @@
 import numpy as np
 import pandas as pd
 from prophet.diagnostics import cross_validation, performance_metrics
+from soda.common.logs import Logs
+from soda.sodacl.anomaly_detection_metric_check_cfg import (
+    HyperparameterConfigs,
+    ProphetDefaultHyperparameters,
+    TrainingDatasetParameters,
+)
 from tqdm import tqdm
 
-from soda.common.logs import Logs
 from soda.scientific.anomaly_detection_v2.exceptions import (
     AggregationValueError,
     FreqDetectionResultError,
@@ -29,11 +34,6 @@
     SuppressStdoutStderr,
     get_not_enough_measurements_freq_result,
 )
-from soda.sodacl.anomaly_detection_metric_check_cfg import (
-    HyperparameterConfigs,
-    ProphetDefaultHyperparameters,
-    TrainingDatasetParameters,
-)
 
 with SuppressStdoutStderr():
     from prophet import Prophet

soda/scientific/tests/anomaly_detection_v2/anomaly_detector_v2_test.py

@@ -12,14 +12,14 @@
     test_anomaly_detector_parsed_historical_measurements,
     test_empty_anomaly_detector_parsed_ad_measurements,
 )
-
 from soda.common.logs import Logs
-from soda.scientific.anomaly_detection_v2.anomaly_detector import AnomalyDetector
 from soda.sodacl.anomaly_detection_metric_check_cfg import (
     ModelConfigs,
     TrainingDatasetParameters,
 )
 
+from soda.scientific.anomaly_detection_v2.anomaly_detector import AnomalyDetector
+
 LOGS = Logs(logging.getLogger(__name__))
 
 

soda/scientific/tests/anomaly_detection_v2/base_model_test.py

@@ -10,17 +10,17 @@
     test_prophet_model_skip_measurements_this_exclusive_previous,
     test_prophet_model_skip_measurements_this_exclusive_previous_expectation,
 )
-
 from soda.common.logs import Logs
-from soda.scientific.anomaly_detection_v2.anomaly_detector import AnomalyDetector
-from soda.scientific.anomaly_detection_v2.models.prophet_model import ProphetDetector
-from soda.scientific.anomaly_detection_v2.pydantic_models import FreqDetectionResult
 from soda.sodacl.anomaly_detection_metric_check_cfg import (
     HyperparameterConfigs,
     ModelConfigs,
     TrainingDatasetParameters,
 )
 
+from soda.scientific.anomaly_detection_v2.anomaly_detector import AnomalyDetector
+from soda.scientific.anomaly_detection_v2.models.prophet_model import ProphetDetector
+from soda.scientific.anomaly_detection_v2.pydantic_models import FreqDetectionResult
+
 LOGS = Logs(logging.getLogger(__name__))
 PARAMS = AnomalyDetector(
     measurements={"results": []},

soda/scientific/tests/anomaly_detection_v2/feedback_processor_test.py

@@ -10,8 +10,8 @@
     test_feedback_processor_seasonality_skip_measurements,
     test_feedback_processor_seasonality_skip_measurements_expectation,
 )
-
 from soda.common.logs import Logs
+
 from soda.scientific.anomaly_detection_v2.feedback_processor import FeedbackProcessor
 
 LOGS = Logs(logging.getLogger(__name__))

soda/scientific/tests/anomaly_detection_v2/frequency_detector_test.py

@@ -2,15 +2,15 @@
 
 import pandas as pd
 from anomaly_detection_v2.utils import generate_random_dataframe
-
 from soda.common.logs import Logs
-from soda.scientific.anomaly_detection_v2.anomaly_detector import AnomalyDetector
-from soda.scientific.anomaly_detection_v2.frequency_detector import FrequencyDetector
 from soda.sodacl.anomaly_detection_metric_check_cfg import (
     ModelConfigs,
     TrainingDatasetParameters,
 )
 
+from soda.scientific.anomaly_detection_v2.anomaly_detector import AnomalyDetector
+from soda.scientific.anomaly_detection_v2.frequency_detector import FrequencyDetector
+
 LOGS = Logs(logging.getLogger(__name__))
 PARAMS = AnomalyDetector(
     measurements={"results": []},

soda/scientific/tests/anomaly_detection_v2/prophet_model_test.py

@@ -17,13 +17,6 @@
     df_prophet_model_setup_fit_predict,
     test_feedback_processor_seasonality_skip_measurements,
 )
-
-from soda.scientific.anomaly_detection_v2.exceptions import (
-    AggregationValueError,
-    FreqDetectionResultError,
-)
-from soda.scientific.anomaly_detection_v2.feedback_processor import FeedbackProcessor
-from soda.scientific.anomaly_detection_v2.models.prophet_model import ProphetDetector
 from soda.sodacl.anomaly_detection_metric_check_cfg import (
     HyperparameterConfigs,
     ProphetDefaultHyperparameters,
@@ -33,6 +26,13 @@
     TrainingDatasetParameters,
 )
 
+from soda.scientific.anomaly_detection_v2.exceptions import (
+    AggregationValueError,
+    FreqDetectionResultError,
+)
+from soda.scientific.anomaly_detection_v2.feedback_processor import FeedbackProcessor
+from soda.scientific.anomaly_detection_v2.models.prophet_model import ProphetDetector
+
 
 def test_with_exit() -> None:
     time_series_df = generate_random_dataframe(size=2, n_rows_to_convert_none=0, frequency="D")

soda/scientific/tests/anomaly_detection_v2/utils.py

@@ -2,18 +2,18 @@
 
 import numpy as np
 import pandas as pd
-
 from soda.common.logs import Logs
-from soda.scientific.anomaly_detection_v2.anomaly_detector import AnomalyDetector
-from soda.scientific.anomaly_detection_v2.frequency_detector import FrequencyDetector
-from soda.scientific.anomaly_detection_v2.models.prophet_model import ProphetDetector
 from soda.sodacl.anomaly_detection_metric_check_cfg import (
     HyperparameterConfigs,
     ModelConfigs,
     ProphetDefaultHyperparameters,
     TrainingDatasetParameters,
 )
 
+from soda.scientific.anomaly_detection_v2.anomaly_detector import AnomalyDetector
+from soda.scientific.anomaly_detection_v2.frequency_detector import FrequencyDetector
+from soda.scientific.anomaly_detection_v2.models.prophet_model import ProphetDetector
+
 
 def generate_random_dataframe(size: int, n_rows_to_convert_none: int, frequency: str = "D") -> pd.DataFrame:
     """